Vehicle traffic and cargo containers transport are undergoing a significant growth rate worldwide (about 10-15% per year). Current security supervision run by both private companies and port authorities is unable to provide a system that will enable efficient traffic control and monitoring in face of the continuing growth. Automation has a major role to play in supporting the efficient handling and capacity required for meeting the growth in container trade.
In accordance with the growing numbers of containers entering ports and crossing state borders there is a rising demand for a better surveillance system to monitor incoming and outgoing traffic. The need is voiced in both the private and public sectors. The need is apparent for systems that can efficiently and automatically identify various data such as license plate numbers, personal identification badges, incoming package or mail labels and other various data which appears in number code or other form of alphanumeric characters. Automated check and inspection gateways operation of this magnitude and sophistication is still not utilized in either factories, public companies and organizations or households today.
Many traffic environments today already employ various levels of automation, including sophisticated traffic management systems for vehicular traffic and terminal operating systems (TOS) for container movement and inventory management.
The automated system described herein include three main Sub-systems:                1. Image-capturing units, (including illumination devices),        2. A software recognition engine, and        3. System application programs        
The image-capturing units must include an optical and illumination effective method able to produce images of a container ID number and/or license plate number with sufficient quality, (focus, resolution, contrast, and uniformity), under all operating and ambience conditions, (sunlight, sun glare, night time, adverse weather conditions). The software recognition engine and application programs must be able to process these images and convert them into data for real-time processing.
The hardware and software must operate in unison, and be able to read the container and vehicle numbers accurately while they are passing through a gate lane, being lifted or lowered by a container crane, sitting on a chassis slot or handled by other container handling equipment. The design and selection of the Image-capturing and software systems has a great impact on the system infrastructure requirements, determining mounting position and constraints for the cameras and illuminators as well as triggering requirements and solutions.
The above applications fail to disclose or teach at least the following:
1. How to achieve and decipher complete credible identification of data received by the system.
2. These applications approach each application separately and do not describe one whole operative system which will provide an effective solution to the need of such a system in a variety of work places and areas.
3. How to carry out a system self check process without outside interference to assess credibility of data analysis.
4. OCR systems, which these applications are based upon, have further developed and the demand for an expanded system which will answer the need of many and be able to carry out a variety of functions is increasing, these applications do not supply such answer as needed.
Thus, there is a demonstrated need for a character recognition system that is capable of providing accurate and precise identification on site, unaffected by outside condition such as weather and visibility, and provides reliable and verifiable results.
Furthermore, there is a need for a multi-functional universal system that will provide character identification in a wide variety of fields with the same success.
Additionally, the system must be able to perform self-testing and data verification to ensure reliable and repeatable data.
The system architecture must be optimized and designed for OCR. Existing recognition systems and method are based on high resolution and/or line scan cameras to capture OCR images, as a result these system generate data that is not always reliable and accurate. In addition these systems are complex, not easy to operate and great energy consumers.
The system should be based on components, which are modular with custom-built features for maximum integration, for example lower power consumption, and computer-system control.
The system should be able to answer individual needs and demands, and offer information that is both accurate and easily and cheaply accessible.